Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations21613
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory80.0 B

Variable types

Numeric9
Categorical1

Alerts

bedrooms is highly overall correlated with sqft_livingHigh correlation
floors is highly overall correlated with grade and 1 other fieldsHigh correlation
grade is highly overall correlated with floors and 3 other fieldsHigh correlation
price is highly overall correlated with grade and 1 other fieldsHigh correlation
sqft_living is highly overall correlated with bedrooms and 2 other fieldsHigh correlation
yr_built is highly overall correlated with floors and 1 other fieldsHigh correlation
sqft_basement has 13126 (60.7%) zeros Zeros

Reproduction

Analysis started2024-12-09 02:03:24.267475
Analysis finished2024-12-09 02:03:33.136598
Duration8.87 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

date
Real number (ℝ)

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean143902.67
Minimum140502
Maximum150527
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-12-08T20:03:33.202750image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum140502
5-th percentile140520
Q1140722
median141016
Q3150217
95-th percentile150427
Maximum150527
Range10025
Interquartile range (IQR)9495

Descriptive statistics

Standard deviation4436.5825
Coefficient of variation (CV)0.030830439
Kurtosis-1.4249889
Mean143902.67
Median Absolute Deviation (MAD)389
Skewness0.75269642
Sum3.1101684 × 109
Variance19683264
MonotonicityNot monotonic
2024-12-08T20:03:33.296530image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140623 142
 
0.7%
140625 131
 
0.6%
140626 131
 
0.6%
140708 127
 
0.6%
150427 126
 
0.6%
150325 123
 
0.6%
150428 121
 
0.6%
150422 121
 
0.6%
140709 121
 
0.6%
150414 121
 
0.6%
Other values (362) 20349
94.2%
ValueCountFrequency (%)
140502 67
0.3%
140503 4
 
< 0.1%
140504 5
 
< 0.1%
140505 84
0.4%
140506 83
0.4%
140507 93
0.4%
140508 81
0.4%
140509 81
0.4%
140510 5
 
< 0.1%
140511 2
 
< 0.1%
ValueCountFrequency (%)
150527 1
 
< 0.1%
150524 1
 
< 0.1%
150515 1
 
< 0.1%
150514 11
 
0.1%
150513 31
0.1%
150512 49
0.2%
150511 40
0.2%
150510 2
 
< 0.1%
150509 3
 
< 0.1%
150508 54
0.2%

floors
Real number (ℝ)

High correlation 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.494309
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-12-08T20:03:33.376505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5399889
Coefficient of variation (CV)0.36136361
Kurtosis-0.48472294
Mean1.494309
Median Absolute Deviation (MAD)0.5
Skewness0.61617672
Sum32296.5
Variance0.29158801
MonotonicityNot monotonic
2024-12-08T20:03:33.451529image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 10680
49.4%
2 8241
38.1%
1.5 1910
 
8.8%
3 613
 
2.8%
2.5 161
 
0.7%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
1 10680
49.4%
1.5 1910
 
8.8%
2 8241
38.1%
2.5 161
 
0.7%
3 613
 
2.8%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
3.5 8
 
< 0.1%
3 613
 
2.8%
2.5 161
 
0.7%
2 8241
38.1%
1.5 1910
 
8.8%
1 10680
49.4%

bedrooms
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3708416
Minimum0
Maximum33
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-12-08T20:03:33.519510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93006183
Coefficient of variation (CV)0.27591383
Kurtosis49.063653
Mean3.3708416
Median Absolute Deviation (MAD)1
Skewness1.9742995
Sum72854
Variance0.86501501
MonotonicityNot monotonic
2024-12-08T20:03:33.596756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 9824
45.5%
4 6882
31.8%
2 2760
 
12.8%
5 1601
 
7.4%
6 272
 
1.3%
1 199
 
0.9%
7 38
 
0.2%
0 13
 
0.1%
8 13
 
0.1%
9 6
 
< 0.1%
Other values (3) 5
 
< 0.1%
ValueCountFrequency (%)
0 13
 
0.1%
1 199
 
0.9%
2 2760
 
12.8%
3 9824
45.5%
4 6882
31.8%
5 1601
 
7.4%
6 272
 
1.3%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 6
 
< 0.1%
8 13
 
0.1%
7 38
 
0.2%
6 272
 
1.3%
5 1601
 
7.4%
4 6882
31.8%
3 9824
45.5%

sqft_living
Real number (ℝ)

High correlation 

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2079.8997
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-12-08T20:03:33.686640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11427
median1910
Q32550
95-th percentile3760
Maximum13540
Range13250
Interquartile range (IQR)1123

Descriptive statistics

Standard deviation918.4409
Coefficient of variation (CV)0.44157941
Kurtosis5.243093
Mean2079.8997
Median Absolute Deviation (MAD)540
Skewness1.4715554
Sum44952873
Variance843533.68
MonotonicityNot monotonic
2024-12-08T20:03:33.796764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 138
 
0.6%
1400 135
 
0.6%
1440 133
 
0.6%
1660 129
 
0.6%
1010 129
 
0.6%
1800 129
 
0.6%
1820 128
 
0.6%
1480 125
 
0.6%
1720 125
 
0.6%
1560 124
 
0.6%
Other values (1028) 20318
94.0%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
8000 1
< 0.1%

sqft_lot
Real number (ℝ)

Distinct9782
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15106.968
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-12-08T20:03:33.906928image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800
Q15040
median7618
Q310688
95-th percentile43339.2
Maximum1651359
Range1650839
Interquartile range (IQR)5648

Descriptive statistics

Standard deviation41420.512
Coefficient of variation (CV)2.7418151
Kurtosis285.07782
Mean15106.968
Median Absolute Deviation (MAD)2618
Skewness13.060019
Sum3.2650689 × 108
Variance1.7156588 × 109
MonotonicityIncreasing
2024-12-08T20:03:34.006994image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 358
 
1.7%
6000 290
 
1.3%
4000 251
 
1.2%
7200 220
 
1.0%
4800 120
 
0.6%
7500 119
 
0.6%
4500 114
 
0.5%
8400 111
 
0.5%
9600 109
 
0.5%
3600 103
 
0.5%
Other values (9772) 19818
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
572 1
< 0.1%
600 1
< 0.1%
609 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
649 2
< 0.1%
651 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
1651359 1
< 0.1%
1164794 1
< 0.1%
1074218 1
< 0.1%
1024068 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
920423 1
< 0.1%
881654 1
< 0.1%
871200 2
< 0.1%
843309 1
< 0.1%

sqft_basement
Real number (ℝ)

Zeros 

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.50905
Minimum0
Maximum4820
Zeros13126
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-12-08T20:03:34.119284image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.57504
Coefficient of variation (CV)1.5182206
Kurtosis2.7155742
Mean291.50905
Median Absolute Deviation (MAD)0
Skewness1.5779651
Sum6300385
Variance195872.67
MonotonicityNot monotonic
2024-12-08T20:03:34.396682image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13126
60.7%
600 221
 
1.0%
700 218
 
1.0%
500 214
 
1.0%
800 206
 
1.0%
400 184
 
0.9%
1000 149
 
0.7%
900 144
 
0.7%
300 142
 
0.7%
200 108
 
0.5%
Other values (296) 6901
31.9%
ValueCountFrequency (%)
0 13126
60.7%
10 2
 
< 0.1%
20 1
 
< 0.1%
40 4
 
< 0.1%
50 11
 
0.1%
60 10
 
< 0.1%
65 1
 
< 0.1%
70 7
 
< 0.1%
80 20
 
0.1%
90 21
 
0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
3500 1
< 0.1%
3480 1
< 0.1%
3260 1
< 0.1%
3000 1
< 0.1%
2850 1
< 0.1%
2810 1
< 0.1%
2730 1
< 0.1%
2720 1
< 0.1%

yr_built
Real number (ℝ)

High correlation 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.0051
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-12-08T20:03:34.569648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.373411
Coefficient of variation (CV)0.014902757
Kurtosis-0.6574075
Mean1971.0051
Median Absolute Deviation (MAD)23
Skewness-0.4698054
Sum42599334
Variance862.79726
MonotonicityNot monotonic
2024-12-08T20:03:34.769533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 559
 
2.6%
2006 454
 
2.1%
2005 450
 
2.1%
2004 433
 
2.0%
2003 422
 
2.0%
2007 417
 
1.9%
1977 417
 
1.9%
1978 387
 
1.8%
1968 381
 
1.8%
2008 367
 
1.7%
Other values (106) 17326
80.2%
ValueCountFrequency (%)
1900 87
0.4%
1901 29
 
0.1%
1902 27
 
0.1%
1903 46
0.2%
1904 45
0.2%
1905 74
0.3%
1906 92
0.4%
1907 65
0.3%
1908 86
0.4%
1909 94
0.4%
ValueCountFrequency (%)
2015 38
 
0.2%
2014 559
2.6%
2013 201
 
0.9%
2012 170
 
0.8%
2011 130
 
0.6%
2010 143
 
0.7%
2009 230
1.1%
2008 367
1.7%
2007 417
1.9%
2006 454
2.1%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size169.0 KiB
0.5
14031 
0.75
5679 
1.0
1701 
0.25
 
172
0.0
 
30

Length

Max length4
Median length3
Mean length3.2707167
Min length3

Characters and Unicode

Total characters70690
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.5
2nd row0.5
3rd row0.5
4th row0.5
5th row0.5

Common Values

ValueCountFrequency (%)
0.5 14031
64.9%
0.75 5679
26.3%
1.0 1701
 
7.9%
0.25 172
 
0.8%
0.0 30
 
0.1%

Length

2024-12-08T20:03:34.919686image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-08T20:03:35.069368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.5 14031
64.9%
0.75 5679
26.3%
1.0 1701
 
7.9%
0.25 172
 
0.8%
0.0 30
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 21643
30.6%
. 21613
30.6%
5 19882
28.1%
7 5679
 
8.0%
1 1701
 
2.4%
2 172
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70690
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21643
30.6%
. 21613
30.6%
5 19882
28.1%
7 5679
 
8.0%
1 1701
 
2.4%
2 172
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70690
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21643
30.6%
. 21613
30.6%
5 19882
28.1%
7 5679
 
8.0%
1 1701
 
2.4%
2 172
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70690
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21643
30.6%
. 21613
30.6%
5 19882
28.1%
7 5679
 
8.0%
1 1701
 
2.4%
2 172
 
0.2%

grade
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55473943
Minimum0
Maximum1
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-12-08T20:03:35.218840image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.41666667
Q10.5
median0.5
Q30.58333333
95-th percentile0.75
Maximum1
Range1
Interquartile range (IQR)0.083333333

Descriptive statistics

Standard deviation0.097954896
Coefficient of variation (CV)0.17657821
Kurtosis1.1909321
Mean0.55473943
Median Absolute Deviation (MAD)0.083333333
Skewness0.7711032
Sum11989.583
Variance0.0095951617
MonotonicityNot monotonic
2024-12-08T20:03:35.377021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.5 8981
41.6%
0.583333333 6068
28.1%
0.666666667 2615
 
12.1%
0.416666667 2038
 
9.4%
0.75 1134
 
5.2%
0.833333333 399
 
1.8%
0.333333333 242
 
1.1%
0.916666667 90
 
0.4%
0.25 29
 
0.1%
1 13
 
0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
0.166666667 3
 
< 0.1%
0.25 29
 
0.1%
0.333333333 242
 
1.1%
0.416666667 2038
 
9.4%
0.5 8981
41.6%
0.583333333 6068
28.1%
0.666666667 2615
 
12.1%
0.75 1134
 
5.2%
0.833333333 399
 
1.8%
ValueCountFrequency (%)
1 13
 
0.1%
0.916666667 90
 
0.4%
0.833333333 399
 
1.8%
0.75 1134
 
5.2%
0.666666667 2615
 
12.1%
0.583333333 6068
28.1%
0.5 8981
41.6%
0.416666667 2038
 
9.4%
0.333333333 242
 
1.1%
0.25 29
 
0.1%

price
Real number (ℝ)

High correlation 

Distinct3625
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540182.16
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size169.0 KiB
2024-12-08T20:03:35.536851image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile210000
Q1321950
median450000
Q3645000
95-th percentile1160000
Maximum7700000
Range7625000
Interquartile range (IQR)323050

Descriptive statistics

Standard deviation367362.23
Coefficient of variation (CV)0.68007102
Kurtosis34.522444
Mean540182.16
Median Absolute Deviation (MAD)150000
Skewness4.0217156
Sum1.1674957 × 1010
Variance1.3495501 × 1011
MonotonicityNot monotonic
2024-12-08T20:03:35.676564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
350000 172
 
0.8%
450000 172
 
0.8%
550000 159
 
0.7%
500000 152
 
0.7%
425000 150
 
0.7%
325000 148
 
0.7%
400000 145
 
0.7%
375000 138
 
0.6%
300000 133
 
0.6%
525000 131
 
0.6%
Other values (3615) 20113
93.1%
ValueCountFrequency (%)
75000 1
< 0.1%
78000 1
< 0.1%
80000 1
< 0.1%
81000 1
< 0.1%
82000 1
< 0.1%
82500 1
< 0.1%
83000 1
< 0.1%
84000 1
< 0.1%
85000 2
< 0.1%
86500 1
< 0.1%
ValueCountFrequency (%)
7700000 1
< 0.1%
7060000 1
< 0.1%
6890000 1
< 0.1%
5570000 1
< 0.1%
5350000 1
< 0.1%
5300000 1
< 0.1%
5110000 1
< 0.1%
4670000 1
< 0.1%
4500000 1
< 0.1%
4490000 1
< 0.1%

Interactions

2024-12-08T20:03:32.246701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:24.687160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:25.456847image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:26.177065image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:26.940553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:27.806776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:28.640190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:29.786665image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:31.111739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:32.319727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:24.756619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:25.531744image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:26.252804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:27.026925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:27.896485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:28.746342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:29.906693image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:31.276602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:32.396702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:24.857215image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:25.603850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:26.335237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:27.126721image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:27.989563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:28.881615image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:30.052907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:31.406717image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:32.469696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:24.937491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:25.685266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:26.406409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:27.226729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:28.076566image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:29.071124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:30.223660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:31.526863image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:32.546583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:25.043620image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:25.773519image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:26.502218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:27.319974image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:28.181875image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:29.206455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:30.396380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:31.656868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:32.619648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:25.126592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:25.852912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:26.576510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:27.422275image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:28.269780image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:29.306444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:30.559094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:31.791980image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:32.696642image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:25.209061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:25.926809image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:26.656430image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:27.506638image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:28.370056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:29.390239image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:30.706850image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:31.926726image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:32.769631image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:25.301420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:26.026666image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:26.746779image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:27.596472image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:28.461501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:29.476688image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:30.826740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:32.066621image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:32.836655image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:25.376697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:26.106562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:26.846866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:27.704640image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:28.546623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:29.706630image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:30.969897image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-08T20:03:32.169576image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-08T20:03:35.761616image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
bedroomsconditiondatefloorsgradepricesqft_basementsqft_livingsqft_lotyr_built
bedrooms1.0000.024-0.0170.2280.3810.3450.2300.6470.2170.180
condition0.0241.0000.0490.1790.1540.0230.0940.0600.0390.248
date-0.0170.0491.000-0.023-0.041-0.011-0.014-0.035-0.013-0.002
floors0.2280.179-0.0231.0000.5020.322-0.2720.401-0.2340.552
grade0.3810.154-0.0410.5021.0000.6580.0930.7160.1520.501
price0.3450.023-0.0110.3220.6581.0000.2520.6440.0750.102
sqft_basement0.2300.094-0.014-0.2720.0930.2521.0000.3280.037-0.178
sqft_living0.6470.060-0.0350.4010.7160.6440.3281.0000.3040.352
sqft_lot0.2170.039-0.013-0.2340.1520.0750.0370.3041.000-0.038
yr_built0.1800.248-0.0020.5520.5010.102-0.1780.352-0.0381.000

Missing values

2024-12-08T20:03:32.949055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-08T20:03:33.069547image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

datefloorsbedroomssqft_livingsqft_lotsqft_basementyr_builtconditiongradeprice
01410221.542420520019000.50.500000700000.0
11501072.03105057231020060.50.500000250000.0
21501222.02103060035020040.50.583333367500.0
31504223.02247060956020110.50.8333331230000.0
41412152.02107063535020080.50.666667256950.0
51406273.011100638020140.50.666667516500.0
61503192.02107064935020080.50.666667259950.0
71503052.02107064935020080.50.666667259950.0
81503033.031060651020070.50.500000405000.0
91504072.01107067519020070.50.583333420000.0
datefloorsbedroomssqft_livingsqft_lotsqft_basementyr_builtconditiongradeprice
216031501072.045545871200194020030.500.833333937500.0
216041504152.046530871200020080.500.8333331600000.0
216051501053.043920881654020020.500.8333331650000.0
216061503202.023900920423020090.500.9166672000000.0
216071407301.022560982278020040.500.583333790000.0
216081409052.043770982998019920.500.750000998000.0
216091501192.0440301024068020060.500.750000855000.0
216101405211.5530101074218100019311.000.583333542500.0
216111505041.027101164794019150.250.333333190000.0
216121503271.0413001651359019200.750.416667700000.0